article

Incorporating contextual information in recommender systems using a multidimensional approach

Authors:

Gediminas Adomavicius,

Ramesh Sankaranarayanan,

Alexander TuzhilinAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 23, Issue 1

Pages 103 - 145

https://doi.org/10.1145/1055709.1055714

Published: 01 January 2005 Publication History

Abstract

The article presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendations. The article also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the article introduces a combined rating estimation method, which identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the article presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance.

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 23, Issue 1

January 2005

145 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/1055709

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Copyright © 2005 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2005

Published in TOIS Volume 23, Issue 1

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